Vessel detection from overhead imagery is currently accomplished manually by human image analysts. The image analysts must scroll across groups of approximately 512 raster lines at a time. Once the end of the line is reached, the search continues across the next set of 512 raster lines directly below the previously-scrolled set. In each frame, the image analyst looks for vessels by eye. Such a task is time consuming and labor intensive since images can be thousands of pixels across and thousands of lines in length. The inclusion in such images of clouds, land, rough seas, etc. often hinders the manual search and makes the search even more tedious.
Automated tools for overhead imagery analysis are currently under development, but these tools either require too many user inputs or output too many false detects. For example, current automated cloud detection algorithms use images and data that contain wavelengths outside of the visible band. Since overhead imagery used in the process of concern is often within the visible band, the cloud detection algorithms from such automated tools are not applicable to an analysis of overhead imagery. As another example, there have been many attempts at filtering and rejecting noise in images containing ocean water with many breaks while still retaining pertinent ship information. One such technique is the Constant False Alarm Rate (CFAR) technique. The CFAR technique is an effective technique that is applied by examining smaller windows within an image and acquiring statistics around each window. Although this noise removal technique shows to be more effective as well as more efficient since noise is removed over the entire image without having to break the image into smaller pieces, this technique is not effective for images in the visible band.
In sum, prior art automated image processing algorithms that are geared for cloud detection/removal, noisy water rejection, wake detection/removal, and wisp detection/removal are based on processes that manipulate data from outside of the visible band. There still exists a vast amount of data, however, collected from sensors that operate only in the visible band.
In view of the above, there is a need in the art for an automated image analysis system and method that effectively detects vessels from overhead imagery containing data that is within the visible spectrum. The system and methods of the present invention provides such an overhead image analysis tool. These and other advantages of the invention, as well as additional inventive features, will be apparent from the description of the invention provided herein.